Amazon Technologies, Inc.

Christopher Wiswall Greene

David Anthony Leen

Kevin Michael McCormick

Aaron Brown

Krishnamurthy Ganesan

Alex Kharbush

Kunal Chandha

Techniques for query-based data modification are described herein. In some examples, a first search of searchable data may be performed based, at least in part, on a first query. A second search of the searchable data may be performed based, at least in part, on a second query. One or more results of the first search may be provided. Input may be received for associating a first result of the first search with the second search. At least a first modification, related to the input, to perform on the searchable data may then be determined. The first modification may then be performed on the searchable data.

Query-based data modification

A browser or browser component queues touch events on a user device while an associated network page is being rendered. The browser may store a detected touch event, a location of the touch event, and/or a vector associated with the touch event in a touch events queue implemented by a data repository. The browser may also visually indicate that the touch event has been registered. Once the network page is at least partially interactive, the browser may retrieve the touch event information from the touch events queue and apply the touch event. Prior to or after applying the touch event, the visual indicator that alerts the user that the touch event was registered may be removed. The browser may then re-render and display the network page after the touch event has been applied.

User-visible touch event queuing

A processing device receives a multi-dimensional input associated with audio data representing an utterance received by a speech-detection device, the multi-dimensional input comprising a speaker identifier (ID) of a speaker of the utterance and a device ID of the speech-detection device. The processing device processes the multi-dimensional input using a first machine learning model associated with at least one of the device ID or the speaker ID to determine a first anomaly rating for the utterance. The processing device determines that the first anomaly rating is above a first anomaly threshold. The processing device determines that the utterance is classified as an anomaly and performs a remedial action.

Anomaly detection for voice controlled devices

Techniques for parsing and execution of data including multiple information classes are described herein. In some examples, a collection of data may include multiple information classes through which the data may be parsed and analyzed. In some examples, the multiple information classes may include a textual character information class, a visual style information class, and an inferred information class, such as may include data identifiable based on information external to the data collection. A plurality of tokens associated with the data collection may be generated. One or more of the plurality of tokens may be organized into a set of instructions. The set of instructions may be provided to a computer program for execution.

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Seattle, WA, United States of America

Christopher Wiswall Greene